# Global Censored Quantile Random Forest

arXiv: https://arxiv.org/abs/2410.12209 · HTML: https://mlsanity.com/paper/2410.12209/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance](https://mlsanity.com/paper/1905.03151/) (2019)
- [Consistency of random forests](https://mlsanity.com/paper/1405.2881/) (2014)
- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [A general framework for inference on algorithm-agnostic variable importance](https://mlsanity.com/paper/2004.03683/) (2020)
- [Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success](https://mlsanity.com/paper/1911.00190/) (2019)
- [Asymptotic Distributions and Rates of Convergence for Random Forests via Generalized U-statistics](https://mlsanity.com/paper/1905.10651/) (2019)
- [Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest](https://mlsanity.com/paper/2103.16700/) (2021)
- [Randomized incomplete U-statistics in high dimensions](https://mlsanity.com/paper/1712.00771/) (2017)
- [Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance](https://mlsanity.com/paper/2003.03629/) (2020)
- [Censored Quantile Regression Forest](https://mlsanity.com/paper/2001.03458/) (2020)
- [Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring](https://mlsanity.com/paper/2012.03294/) (2020)
- [Interval censored recursive forests](https://mlsanity.com/paper/1912.09983/) (2019)
- [Hybrid Censored Quantile Regression Forest to Assess the Heterogeneous Effects](https://mlsanity.com/paper/2212.05672/) (2022)

## Abstract (arXiv)

In recent years, censored quantile regression has enjoyed an increasing popularity for survival analysis while many existing works rely on linearity assumptions. In this work, we propose a Global Censored Quantile Random Forest (GCQRF) for predicting a conditional quantile process on data subject to right censoring, a forest-based flexible, competitive method able to capture complex nonlinear relationships. Taking into account the randomness in trees and connecting the proposed method to a randomized incomplete infinite degree U-process (IDUP), we quantify the prediction process' variation without assuming an infinite forest and establish its weak convergence. Moreover, feature importance ranking measures based on out-of-sample predictive accuracy are proposed. We demonstrate the superior predictive accuracy of the proposed method over a number of existing alternatives and illustrate the use of the proposed importance ranking measures on both simulated and real data.